import requests
import json
import re

class LLMAcousticBridge:
    def __init__(self, model_name="dolphin-llama3:8b"):
        self.model_name = model_name
        self.api_url = "http://localhost:11434/api/generate"

    def _clean_json(self, text):
        """Вытаскивает чистый JSON из ответа нейросети."""
        try:
            match = re.search(r'\{.*\}', text, re.DOTALL)
            if match:
                return json.loads(match.group(0))
            return json.loads(text)
        except:
            return None

    def get_acoustic_profile(self, valence, arousal, scene_descriptions):
        """Просит LLM сгенерировать идеальный звук под описание."""
        # Объединяем описания, если загружено несколько фото
        context_str = " | ".join(scene_descriptions) if scene_descriptions else "abstract scene"
        
        prompt = f"""You are an expert music producer and acoustic engineer. 
Analyze the visual context and emotions to determine the ideal background music properties.
Emotions: Valence {valence:.1f}/9.0 (Positivity), Arousal {arousal:.1f}/9.0 (Energy).
Visual Context: {context_str}.

Map this scene to exactly 6 acoustic features. Values MUST be floats between 0.0 and 1.0.
1. "energy": (Loudness/Density. High for massive/busy scenes, Low for calm)
2. "flux": (Rhythmic sharpness/Beat. High for action/people/cars, Low for static nature)
3. "centroid": (Brightness: 0=Dark/Bass/Massive, 1=Bright/Treble/Light)
4. "pitch": (Fundamental frequency: 0=Low pitch/Huge objects, 1=High pitch/Small objects)
5. "hnr": (Harmonics-to-Noise: 0=Noisy/Distorted textures, 1=Clear/Melodic/Smooth textures)
6. "zcr": (Percussiveness. High for detailed noise like leaves/rain, Low for solid blocks)

Return ONLY a valid JSON object. Do not add any text or explanation.
Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}"""

        try:
            response = requests.post(self.api_url, json={
                "model": self.model_name,
                "prompt": prompt,
                "stream": False,
                "format": "json"
            }, timeout=30)
            response.raise_for_status()
            
            result_text = response.json().get("response", "")
            profile = self._clean_json(result_text)
            
            # Проверяем, что все нужные ключи есть
            required_keys = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
            if profile and all(k in profile for k in required_keys):
                return profile
            return None
        except Exception as e:
            print(f"Ошибка связи с локальной LLM: {e}")
            return None